Plan-Based Dialogue Management in a Physics Tutor

This paper describes an application of APE (the Atlas Planning Engine), an integrated planning and execution system at the heart of the Atlas dialogue management system. APE controls a mixed-initiative dialogue between a human user and a host system, where turns in the 'conversation' may include graphical actions and/or written text. APE has full unification and can handle arbitrarily nested discourse constructs, making it more powerful than dialogue managers based on finitestate machines. We illustrate this work by describing Atlas-Andes, an intelligent tutoring system built using APE with the Andes physics tutor as the host.

[1]  Reva Freedman,et al.  Responding to Unexpected Student Utterances in CIRCSIM-Tutor v.3: Analysis of Transcripts , 1998, FLAIRS.

[2]  Martha W. Evens,et al.  Spelling Correction using Context , 1998, ACL.

[3]  John Cook,et al.  Knowledge mentoring as a framework for designing computer-based agents for supporting musical composition learning , 1998 .

[4]  Reva Freedman Using a Reactive Planner as the Basis for a Dialogue Agent , 2000, FLAIRS Conference.

[5]  Michael E. Bratman,et al.  What is intention , 1987 .

[6]  Michael Wooldridge,et al.  The Belief-Desire-Intention Model of Agency , 1998, ATAL.

[7]  Qiang Yang,et al.  Formalizing planning knowledge for hierarchical planning , 1990, Comput. Intell..

[8]  David E. Wilkins,et al.  Practical planning - extending the classical AI planning paradigm , 1989, Morgan Kaufmann series in representation and reasoning.

[9]  Kurt VanLehn,et al.  Conceptual and Meta Learning During Coached Problem Solving , 1996, Intelligent Tutoring Systems.

[10]  Carolyn Penstein Rosé,et al.  A framework for robust semantic interpretation , 2000 .

[11]  Joel A. Michael,et al.  Dynamic instructional planning for an intelligent physiology tutoring system , 1991, [1991] Computer-Based Medical Systems@m_Proceedings of the Fourth Annual IEEE Symposium.

[12]  Philip R. Cohen,et al.  Intentions in Communication , 1992, Language.

[13]  Martha E. Pollack,et al.  The Uses of Plans , 1992, Artif. Intell..

[14]  Jérôme Lehuen,et al.  Un modèle hypothético-expérimental dynamique pour la gestion des dialogues homme-machine , 1996 .

[15]  Cristina Conati,et al.  Procedural Help in Andes: Generating Hints Using a Bayesian Network Student Model , 1998, AAAI/IAAI.

[16]  Michael P. Georgeff,et al.  Decision-Making in an Embedded Reasoning System , 1989, IJCAI.

[17]  Julita Vassileva,et al.  Reactive Instructional Planning to Support Interacting Teaching Strategies , 1995 .

[18]  Etienne Wenger,et al.  Artificial Intelligence and Tutoring Systems , 1987 .

[19]  Reva Freedman,et al.  Delivering Hints in a Dialogue-Based Intelligent Tutoring System , 1999, AAAI/IAAI.

[20]  InterpretationCarolyn P. Ros A Framework for Robust Semantic Interpretation , 2000 .

[21]  James A. Hendler,et al.  HTN Planning: Complexity and Expressivity , 1994, AAAI.

[22]  John D. Lowrance,et al.  Planning and reacting in uncertain and dynamic environments , 1995, J. Exp. Theor. Artif. Intell..

[23]  David J. Israel,et al.  Plans and resource‐bounded practical reasoning , 1988, Comput. Intell..

[24]  Etienne Wenger,et al.  Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge , 1987 .

[25]  Candace L. Sidner,et al.  Attention, Intentions, and the Structure of Discourse , 1986, CL.

[26]  J. Sinclair,et al.  Towards an Analysis of Discourse: The English Used by Teachers and Pupils , 1975 .

[27]  B. Woolf Context Dependent Planning in a Machine Tutor , 1984 .